Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification

Seongkyu Mun, Suwon Shon, Wooil Kim, David K. Han, Hanseok Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

Deep Neural Network (DNN) based transfer learning has been shown to be effective in Visual Object Classification (VOC) for complementing the deficit of target domain training samples by adapting classifiers that have been pre-trained for other large-scaled DataBase (DB). Although there exists an abundance of acoustic data, it can also be said that datasets of specific acoustic scenes are sparse for training Acoustic Scene Classification (ASC) models. By exploiting VOC DNN's ability of learning beyond its pre-trained environments, this paper proposes DNN based transfer learning for ASC. Effectiveness of the proposed method is demonstrated on the database of IEEE DCASE Challenge 2016 Task 1 and home surveillance environment via representative experiments. Its improved performance is verified by comparing it to prominent conventional methods.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages796-800
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 2017 Jun 16
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 2017 Mar 52017 Mar 9

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period17/3/517/3/9

Fingerprint

Acoustics
Classifiers
Deep neural networks
Experiments

Keywords

  • acoustic scene classification
  • deep neural network
  • mid-level feature
  • Transfer learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Mun, S., Shon, S., Kim, W., Han, D. K., & Ko, H. (2017). Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 796-800). [7952265] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7952265

Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification. / Mun, Seongkyu; Shon, Suwon; Kim, Wooil; Han, David K.; Ko, Hanseok.

2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 796-800 7952265.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mun, S, Shon, S, Kim, W, Han, DK & Ko, H 2017, Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings., 7952265, Institute of Electrical and Electronics Engineers Inc., pp. 796-800, 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, New Orleans, United States, 17/3/5. https://doi.org/10.1109/ICASSP.2017.7952265
Mun S, Shon S, Kim W, Han DK, Ko H. Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 796-800. 7952265 https://doi.org/10.1109/ICASSP.2017.7952265
Mun, Seongkyu ; Shon, Suwon ; Kim, Wooil ; Han, David K. ; Ko, Hanseok. / Deep Neural Network based learning and transferring mid-level audio features for acoustic scene classification. 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 796-800
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